Modeling and optimization of R-717 and R-134a ice thermal energy storage air conditioning systems using NSGA-II and MOPSO algorithms

Abstract In this study, an Ice Thermal Energy Storage (ITES) is integrated to an office building air-conditioning system as a full load storage system. The R-134a and R-717 refrigeration systems by exergy, economic and environmental considerations are modeled and investigated separately. Two multi-objective optimization algorithms: Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO), are engaged to obtain the optimal design parameters which lead to the optimal objective functions, exergy efficiency and total cost rate. The optimum point from Pareto frontier of each optimization algorithm is selected for both refrigerants by using TOPSIS decision making method, and energy demand of the new system is compared to the conventional one. The results indicated that by using NSGA-II and MOPSO algorithms for the R-717 refrigerant based system, the optimum design parameters lead to electricity consumption decrease by 11% and 8% more than R-134a refrigerant based system, respectively. Furthermore, the results showed that NSGA-II is more capable to achieve more effective solutions. At the optimum point of NSGA-II for R-717 based system, the exergy efficiency and total cost rate are 49% and 255USDh−1 respectively. While the annual CO2 emission is 11% lower than the R-134a based system.

[1]  Ali Shirazi,et al.  Thermo-economic optimization of an ice thermal energy storage system for air-conditioning applications , 2013 .

[2]  M. J. Moran,et al.  Thermal design and optimization , 1995 .

[3]  Robin Smith,et al.  Chemical Process: Design and Integration , 2005 .

[4]  Charles M. Gottschalk.  Industrial Energy Conservation , 1996 .

[5]  Seyed Taghi Akhavan Niaki,et al.  An efficient genetic algorithm to maximize net present value of project payments under inflation and bonus-penalty policy in resource investment problem , 2010, Adv. Eng. Softw..

[6]  I. Dincer.  Refrigeration Systems and Applications , 2003 .

[7]  William D. Chvala.  Technology Potential of Thermal Energy Storage (TES) Systems in Federal Facilities , 2002 .

[8]  Sheryl Cates,et al.  Modelling the technical and economic potential of thermal energy storage systems using pseudo-data analysis , 1994 .

[9]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[10]  Roy Joseph Dossat.  Principles of Refrigeration , 1961 .

[11]  Fabio Rinaldi,et al.  Exergetic, economic and environmental (3E) analyses, and multi-objective optimization of a CO2/NH3 cascade refrigeration system , 2014 .

[12]  Fu Xiao,et al.  Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: A review , 2013 .

[13]  Hassan Hajabdollahi,et al.  Evaluation of cooling and thermal energy storage tanks in optimization of multi-generation system , 2015 .

[14]  Tzong-Shing Lee,et al.  Thermodynamic analysis of optimal condensing temperature of cascade-condenser in CO2/NH3 cascade refrigeration systems , 2005 .

[15]  Haji Hassan Masjuki,et al.  Thermodynamic evaluation of utilizing different ice thermal energy storage systems for cooling application in office buildings in Malaysia , 2012 .

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[18]  Z. Yue.  A method for group decision-making based on determining weights of decision makers using TOPSIS , 2011 .

[19]  George W. Irwin,et al.  Intelligent Computing in Signal Processing and Pattern Recognition: International Conference on Intelligent Computing, ICIC 2006Kunming, China, August, ... Notes in Control and Information Sciences) , 2006 .

[21]  Fabio Rinaldi,et al.  Thermal–economic–environmental analysis and multi-objective optimization of an internal-reforming solid oxide fuel cell–gas turbine hybrid system , 2012 .

[22]  Zhiqiang Zhai,et al.  Particle swarm optimization for redundant building cooling heating and power system , 2010 .

[23]  Hoseyn Sayyaadi,et al.  Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system , 2013 .

[24]  Ibrahim Dincer,et al.  On thermal energy storage systems and applications in buildings , 2002 .

[25]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[26]  Ali Behbahaninia,et al.  Thermoeconomic optimization and exergy analysis of CO 2/NH 3 cascade refrigeration systems , 2011 .

[27]  Fabio Rinaldi,et al.  Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling , 2014 .

[28]  Hoseyn Sayyaadi,et al.  Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger , 2012 .

[29]  Pradeep Bansal,et al.  Thermodynamic analysis of an R744-R717 cascade refrigeration system , 2008 .